Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

被引:15
作者
Wang, Xing [1 ,2 ]
Zhou, Tong [1 ,3 ,4 ]
Tao, Fei [1 ,4 ]
Zang, Fengyi [2 ]
机构
[1] Nantong Univ, Sch Geog Sci, Nantong 226007, Peoples R China
[2] Nantong Univ, Sch Econ & Management, Nantong 226019, Peoples R China
[3] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China
[4] Nanjing Normal Univ, Key Lab Virtual Geog Environm, MOE, Nanjing 210046, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 23期
基金
中国国家自然科学基金;
关键词
Luojia1-01; urban heat island; urban building density; land surface temperature; geographically weighted regression; AUTOMATIC ROAD EXTRACTION; BUILDING DENSITY; URBAN DENSITY; LAND-COVER; ENERGY USE; HEIGHT; DYNAMICS; DMSP/OLS; IMPACT; CONSUMPTION;
D O I
10.3390/app9235224
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The urban heat island (UHI) is one of the essential phenomena of the modern urban climate. In recent years, urbanization in China has gradually accelerated, and the heat island effect has also intensified as the urban impervious surface area and the number of buildings is increasing. Urban building density (UBD) is one of the main factors affecting UHI, but there is little discussion on the relationship between the two. This paper takes Hefei as the research area, combines UBD data estimated by Luojial-01 night-time light (NTL) imagery as the research object with land surface temperature (LST) data obtained from Landsat8 images, and carries out spatial correlation analysis on 0.5 x 0.5 km to 2 x 2 km resolution for them, so as to explore the relationship between UBD and UHI. The results show the following: (1) Luojial-01 data have a good ability to estimate UBD and have fewer errors when compared with the actual UBD data; (2) At the four spatial scales, UBD and LST present a significant positive correlation that increases with the enlargement of the spatial scale; and (3) Moreover, the fitting effect of the Geographically Weighted Regression (GWR) model is better than that of the ordinary least squares (OLS) regression model.
引用
收藏
页数:20
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